Xingyu Zhang

Harbin Institute of Technology

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Xia Bi

Shanghai University of Medicine and Health Sciences

Oscar C. Au

Hong Kong University of Science and Technology

Pingping Han

University of Queensland

Lanxia Meng

Wuhan University

Zhentao Zhang

Wuhan University

Yining Zhao

Jiangnan University

Lingyan Zhou

Hangzhou Cancer Hospital

Xia Zou

PLA Army Engineering University

Jiaolong Yang

Wuhan University

Jiehui Chen

Shenzhen University Health Science Center

Cooperative Institutions

Wuhan University

Renmin Hospital of Wuhan University

Chinese Academy of Sciences

Taiyuan University of Technology

Shanghai University of Medicine and Health Sciences

Peking University

Zhejiang University

University of Chinese Academy of Sciences

University of Hong Kong

Shanghai Jiao Tong University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Robust Speaker Recognition Using Improved GFCC and Adaptive Feature Selection

Advances in intelligent systems and computing (2019)

Xingyu Zhang Xia Zou Meng Sun Penglong Wu

Mel-frequency cepstrum

Robustness

Normalization

10.1007/978-3-030-16946-6_13

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Citations (3)

Preparation of single atom catalysts for high sensitive gas sensing

International Journal of Extreme Manufacturing (2024)

Xinxin He Ping Guo Xuyang An Yuyang Li Jiatai Chen

Highlights The single atom catalysts for high sensitive and selective gas sensor. A comprehensive overview of preparation strategies for single atom catalysts. The interactions between the single atom catalysts and target gases as well as their supports. The typical applications of single atom catalysts in normal gases and volatile organic compounds.

10.1088/2631-7990/ad3316

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Citations (3)

Corrigendum: The neutrophil-to-lymphocyte ratio is associated with mild cognitive impairment in community-dwelling older women aged over 70 years: a population-based cross-sectional study

Frontiers in Aging Neuroscience (2023)

Shengjie Li Xiaoyu Chen Mengze Gao Xingyu Zhang Peipei Han

[This corrects the article DOI: 10.3389/fnagi.2023.1261026.].

Cross-sectional study

10.3389/fnagi.2023.1297736

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TREM2 ectodomain and its soluble form in Alzheimer’s disease

Journal of Neuroinflammation (2020)

Jiaolong Yang Zhihui Fu Xingyu Zhang Min Xiong Lanxia Meng

Triggering receptor expressed on myeloid cells 2 (TREM2) is a receptor mainly expressed on the surface of microglia. It mediates multiple pathophysiological processes in various diseases. Recently, TREM2 has been found to play a role in the development of Alzheimer's disease (AD). TREM2 is a transmembrane protein that is specifically expressed on microglia in the brain. It contains a long ectodomain that directly interacts with the extracellular environment to regulate microglial function. The ectodomain of TREM2 is processed by a disintegrin and metalloprotease, resulting in the release of a soluble form of TREM2 (sTREM2). Recent studies have demonstrated that sTREM2 is a bioactive molecule capable of binding ligands, activating microglia, and regulating immune responses during the AD continuum. Clinical studies revealed that sTREM2 level is elevated in cerebrospinal fluid (CSF) of AD patients, and the sTREM2 level is positively correlated with the levels of classical CSF biomarkers, namely t-tau and p-tau, indicating that it is a reliable predictor of the early stages of AD. Herein, we summarize the key results on the generation, structure, and function of sTREM2 to provide new insights into TREM2-related mechanisms underlying AD pathogenesis and to promote the development of TREM2-based therapeutic strategy.

TREM2

Ectodomain

ADAM10

10.1186/s12974-020-01878-2

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Citations (87)

Rate-Distortion Optimized Transforms Based on the Lloyd-Type Algorithm for Intra Block Coding

IEEE Journal of Selected Topics in Signal Processing (2013)

Feng Zou Oscar C. Au Chao Pang Jingjing Dai Xingyu Zhang

The directional intra prediction (IP) in H.264/AVC and HEVC tends to cause the residue to be anisotropic. To transform the IP residue, Mode Dependent Directional Transform (MDDT) based on Karhunen Loève transform (KLT) can achieve better energy compaction than DCT, with one transform assigned to each prediction mode. However, due to the data variation, different residue blocks with the same IP mode may not have the same statistical properties. Instead of constraining one transform for each IP mode, in this paper, we propose a novel rate-distortion optimized transform (RDOT) scheme which allows a set of specially trained transforms to be available to all modes, and each block can choose its preferred transform to minimize the rate-distortion (RD) cost. We define a cost function which is an estimate of the true RD cost and use the Lloyd-type algorithm (a sequence of transform optimization and data reclassification alternately) to find the optimal set of transforms. The proposed RDOT scheme is implemented in HM9.0 software of HEVC. Experimental results suggest that RDOT effectively achieves 1.6% BD-Rate reduction under the Intra Main condition and 1.6% BD-Rate reduction under the Intra High Efficiency (HE) 10bit condition.

Lapped transform

Rate–distortion optimization

Rate distortion

Karhunen–Loève theorem

Algorithmic efficiency

10.1109/jstsp.2013.2274173

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Citations (16)

Convolutional Recurrent Neural Network with Multi-Scale Kernels on Dynamic Connectivity Network for AD Classification

Xingyu Zhang Biao Jie Jianhui Wang

Deep learning methods, including convolutional neural networks (CNNs) and recurrent neural network (RNN), have been used for analysis of brain network, e.g., dynamic functional connectivity (dFC) network. However, CNN usually extract local features of brain network, ignoring the temporal information of dFC network. In addition, diversity feature representations of brain network can be obtained using convolutional kernels with different scales, these representations may contain complementary information that could be used for further improving the diagnosis performance of brain disease (e.g., Alzheimer's Disease, AD). To address this problem, in this paper, we propose a convolutional recurrent neural network with multi-scale kernels (MSK-CRNN) learning framework for brain disease classification with fMRI data. Specifically, we build a convolutional layer with multi-scale kernels to extract different-yet-complementary features from constructed dFC networks, and use a long short-term memory (LSTM) layer to further extract temporal information of dFC networks. The experimental results on 174 subjects with 563 scans from Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that, compared with the existing methods, the proposed MSK-CRNN method can further improve the performance of AD classification.

Feature (linguistics)

10.1145/3583788.3583798

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Diverse role of NMDA receptors for dendritic integration of neural dynamics

PLoS Computational Biology (2023)

Yuanhong Tang Xingyu Zhang Lingling An Zhaofei Yu Jian K. Liu

Neurons, represented as a tree structure of morphology, have various distinguished branches of dendrites. Different types of synaptic receptors distributed over dendrites are responsible for receiving inputs from other neurons. NMDA receptors (NMDARs) are expressed as excitatory units, and play a key physiological role in synaptic function. Although NMDARs are widely expressed in most types of neurons, they play a different role in the cerebellar Purkinje cells (PCs). Utilizing a computational PC model with detailed dendritic morphology, we explored the role of NMDARs at different parts of dendritic branches and regions. We found somatic responses can switch from silent, to simple spikes and complex spikes, depending on specific dendritic branches. Detailed examination of the dendrites regarding their diameters and distance to soma revealed diverse response patterns, yet explain two firing modes, simple and complex spike. Taken together, these results suggest that NMDARs play an important role in controlling excitability sensitivity while taking into account the factor of dendritic properties. Given the complexity of neural morphology varying in cell types, our work suggests that the functional role of NMDARs is not stereotyped but highly interwoven with local properties of neuronal structure.

Dendritic spike

Biological neural network

Dendrite (mathematics)

10.1371/journal.pcbi.1011019

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Citations (1)

Edge-based predictive scanning scheme of DCT coefficients for inter-frame video coding

Xingyu Zhang Oscar C. Au Feng Zou Run Cha Jiali Li

Scanning of quantized transform coefficients is a very significant procedure in video coding. In H.264, it affects the coding efficiency of the following CABAC or CAVLC entropy coder directly. In this paper, we propose a novel edge-based predictive scanning scheme to improve the coding efficiency for inter-frame coding. This scheme includes three scanning pattern candidates. Besides zigzag pattern as defined in H.264, two alternative patterns are obtained by on-line training on frame level. Specifically, reference block is utilized to predict edge information in current 8×8 block. Based on predictive edge information, a suitable scanning pattern will be selected to scan the quantized coefficients of current 8×8 block. Since a similar prediction process can be done in the decoder side as well, no overhead is needed to be transmitted in the bit-stream. Experimental results show that the proposed edge-based predictive scanning scheme yields an average of 0.42% BD-bitrate reduction over the H.264 high profile.

Context-adaptive variable-length coding

Macroblock

Algorithmic efficiency

10.1109/icdsp.2011.6004907

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Sparse-learning-based High-order Dynamic Functional Connectivity Networks for Brain Disease Classification

Jianhui Wang Biao Jie Xingyu Zhang Wen Li Zhaoxiang Wu

Dynamic functional connectivity network (DFCN) derived from resting-state functional magnetic resonance imaging (rs-fMRI), which characterizes the dynamic interaction between brain regions, has been applied to classification of brain diseases. However, existing studies usually focus on dynamic changes of low-order (i.e., pairwise) correlation of brain regions, thus neglecting their high-order dynamic information that could be important for brain disease diagnosis. Therefore, in this paper, we first propose a novel sparse learning based high-order DFCNs construction method, and then build a novel learning framework to extract high-level and high-order temporal features from the constructed high-order DFCNs for brain disease classification. The experimental results on 174 subjects from from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of our proposed method in comparison with state-of-the-art methods.

Dynamic functional connectivity

Brain disease

10.1145/3583788.3583812

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Citations (1)

Beta-informativeness-diffusion multilayer graph embedding for brain network analysis

Frontiers in Neuroscience (2024)

Yin Huang Ying Li Yuting Yuan Xingyu Zhang Wenjie Yan

Brain network analysis provides essential insights into the diagnosis of brain disease. Integrating multiple neuroimaging modalities has been demonstrated to be more effective than using a single modality for brain network analysis. However, a majority of existing brain network analysis methods based on multiple modalities often overlook both complementary information and unique characteristics from various modalities. To tackle this issue, we propose the Beta-Informativeness-Diffusion Multilayer Graph Embedding (BID-MGE) method. The proposed method seamlessly integrates structural connectivity (SC) and functional connectivity (FC) to learn more comprehensive information for diagnosing neuropsychiatric disorders. Specifically, a novel beta distribution mapping function (beta mapping) is utilized to increase vital information and weaken insignificant connections. The refined information helps the diffusion process concentrate on crucial brain regions to capture more discriminative features. To maximize the preservation of the unique characteristics of each modality, we design an optimal scale multilayer brain network, the inter-layer connections of which depend on node informativeness. Then, a multilayer informativeness diffusion is proposed to capture complementary information and unique characteristics from various modalities and generate node representations by incorporating the features of each node with those of their connected nodes. Finally, the node representations are reconfigured using principal component analysis (PCA), and cosine distances are calculated with reference to multiple templates for statistical analysis and classification. We implement the proposed method for brain network analysis of neuropsychiatric disorders. The results indicate that our method effectively identifies crucial brain regions associated with diseases, providing valuable insights into the pathology of the disease, and surpasses other advanced methods in classification performance.

Modalities

Modality (human–computer interaction)

Network Analysis

Discriminative model

Graph Embedding

10.3389/fnins.2024.1303741

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Citations (1)